---
title: "nCov 19 - As on 27/3/2020"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    social: [ "linkedin", "facebook", "menu"]
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(coronavirus)
library(lubridate)
library(janitor)
```


```{r}
data <-read_csv("covid_19_data.csv") %>% 
  filter(ObservationDate=="03/27/2020") %>% 
  clean_names()

data_raw<-read_csv("covid_19_data.csv") %>% 
  clean_names()
```

Summary
===========================================

Row
------------------------------------------


### Alert Level

```{r}
valueBox(paste("4"),
         color = "red",
         icon = "fa-calendar")
```

### Total Cases

```{r}
valueBox(sum(data$confirmed),
         icon="fa-user")

```

### Total Deaths
```{r}

gauge((sum(data$deaths)),
            min = 0,
            max = 100000,
            gaugeSectors(success = c(0, 25000),
                         warning = c(25001, 50000),
                         danger = c(50001, 100000),
                         colors = c("yellow", "orange", "red")))


```

### Total Cases in NZ
```{r}
valueBox(data %>%
           filter(country_region=="New Zealand") %>% 
           select(confirmed) %>% 
           colSums(),
         icon="fa-user")

```

Row
------------------------------------

```{r}
data(worldgeojson, package = "highcharter")

data<-data %>% 
  clean_names() %>% 
  filter(!country_region %in% 'Others') %>% 
  group_by(country_region) %>% 
  summarise(total_confirmed = sum(confirmed)) 


highchart() %>%
  hc_add_series_map(worldgeojson, data, value = 'total_confirmed', joinBy = c('name','country_region'))  %>% 
  #hc_colors(c("darkorange", "darkgray")) %>% 
  hc_colorAxis(stops = color_stops()) %>% 
  hc_title(text = "Countries with nCov exposure")

```

Trends
===============================================


Row
---------------------------


```{r}
p1 <- data_raw %>%
  mutate(observation_date=as.Date(observation_date, format="%m/%d/%Y")) %>% 
  group_by(observation_date) %>% 
  summarise(confirmed =  sum(confirmed) , deaths = sum(deaths), recovered = sum(recovered)) %>% 
  ggplot(aes(observation_date, (confirmed))) +
  geom_line() +
  theme_minimal() +
  ggtitle("Worldwide Cases")

ggplotly(p1)

```


```{r}
p1 <- data_raw %>%
  filter(country_region=="New Zealand") %>% 
  mutate(observation_date=as.Date(observation_date, format="%m/%d/%Y")) %>% 
  group_by(observation_date) %>% 
  summarise(confirmed =  sum(confirmed) , deaths = sum(deaths), recovered = sum(recovered)) %>% 
  ggplot(aes(observation_date, (confirmed))) +
  geom_line() +
  theme_minimal() +
  ggtitle("NZ Cases")

ggplotly(p1)
```